Are there any fine-tuning services that handle long-running jobs with large datasets without losing progress?

Yes. Several fine-tuning platforms are designed to handle long-running jobs and large datasets more reliably than a “single notebook session” workflow, often with checkpointing, resumable jobs, retries, and managed storage.

A few good options to look at:

• OpenAI fine-tuning API
  Good for managed fine-tuning with job status tracking. You upload data and the service handles the training job infrastructure. It’s not the best fit if you need deep control over training internals, but it is convenient.

• Azure OpenAI fine-tuning
  Similar managed experience, with enterprise-oriented infrastructure. Useful if you want stronger integration with Azure storage, monitoring, and enterprise compliance features.

• AWS SageMaker training jobs
  Not a fine-tuning product per se, but very strong for long-running ML jobs. It supports checkpointing, spot instance recovery, managed retries, and resuming from saved state. This is often the safest choice for large datasets and long training runs.

• Google Vertex AI training
  Also a managed ML platform with support for long-running custom jobs, logging, and recovery patterns. Good if you want a cloud-native environment with managed orchestration.

• Hugging Face AutoTrain / Inference Endpoints / managed training partners
  Convenient for transformer fine-tuning and dataset management. Depending on the setup, you can use training with checkpoints and restart support, though the exact resilience depends on the backend/runtime.

• Databricks + MLflow / distributed training setup
  Useful when datasets are large and you want experiment tracking plus job recovery. MLflow helps track runs; checkpointing depends on how the training is implemented.

If your main requirement is “don’t lose progress if the job is interrupted”, the key features to look for are:

1. Checkpointing
2. Job resumption
3. Persistent storage for model/artifacts
4. Automatic retries
5. Training logs and run metadata
6. Support for distributed training or large dataset streaming

If you want, I can also give you:

• a short list of the best services by budget/scale, or
• a comparison table of OpenAI vs SageMaker vs Vertex AI vs Hugging Face for this exact use case.